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Influence of ABCB1 genetic variants in breast cancer treatment outcomes
Cancer Epidemiology 37 (2013) 754–761
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Cancer Epidemiology The International Journal of Cancer Epidemiology, Detection, and Prevention journal homepage: www.cancerepidemiology.net
Influence of ABCB1 genetic variants in breast cancer treatment outcomes P. Chaturvedi a,1, S. Tulsyan a,1, G. Agarwal b, P. Lal c, S. Agarwal c, R.D. Mittal d, B. Mittal a,* a
Department of Genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India Department of Endocrine & Breast Surgery, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India c Department of Radiotherapy, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India d Department of Urology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 11 January 2013 Received in revised form 20 March 2013 Accepted 26 April 2013 Available online 23 May 2013
Background: Transportation of anticancer drugs such as anthracyclines across the membrane is regulated by P-glycoprotein encoded by the human multidrug resistance gene 1 (ABCB1). Polymorphisms in the ABCB1 gene (1236C > T, 2677G > T/A, 3435C > T) have been found to be associated with intrinsic and acquired cross resistance to these anticancer drugs. Therefore, the aim of this study is to evaluate the influence of ABCB1 gene polymorphisms in breast cancer treatment outcomes in terms of response and toxicity. Method: Response to neo-adjuvant chemotherapy was evaluated in 100 patients while grade 2–4 toxicity was followed in 207 patients, who had undergone FEC/FAC chemotherapy. Genotyping for ABCB1 polymorphisms was done by PCR-RFLP. Chi square and logistic regression analyses were used to calculate Odd’s ratio using SPSS ver 17.0. A meta analysis was also performed using Comprehensive Meta Analysis Ver 2. Results: In response evaluation, 1236C > T polymorphism was significantly associated with treatment response for CT genotype [OR = 5.17(1.3–20.2), P = 0.018] and in dominant model (CC vs CT + TT) [OR = 4.63(1.25–17.0), P = 0.021]. In the toxicity group, the T allele of 1236C>T was associated with grade 2–4 tocxicity [OR 1.48(1.00–2.20), P = 0.049] and the association was also significant in the recessive model [OR 1.88(1.05–3.39), P = 0.033]. For other two SNPs 2677G>T/A and 3435C>T no association was seen with either treatment response or grade 2–4 toxicity. In meta analysis, no overall association was found. Conclusion: In our study, significant association was seen for ABCB1 1236C>T polymorphism with treatment response. The meta analysis did not show overall association with treatment outcomes. ß 2013 Elsevier Ltd. All rights reserved.
Keywords: Breast cancer Multi drug resistance gene Polymorphism Treatment outcomes Response Toxicity
1. Introduction Breast cancer patients are treated with chemotherapy depending upon the stage, e.g. locally advanced breast cancers are treated with neo-adjuvant chemotherapy (NACT) while early breast cancers are treated with primary surgery followed by adjuvant chemotherapy (ACT). Treatment strategies include chemotherapy – anthracyclines (epirubicin/doxorubicin), cytotoxics (cyclophosphamide, paclitaxel, docetaxel), hormone therapy anti-estrogens (tamoxifen) and aromatase inhibitors (exemestane, anastrozole, letrozole). A significant heterogeneity is observed in the response and toxicity to chemotherapeutic agents [1,2]. Genetic differences in drug transporters, enzymes of primary and secondary
* Corresponding author at: Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow 226 014, India. Tel.: +91 522 249 4322; fax: +91 522 2668973. E-mail addresses: [email protected], [email protected] (B. Mittal). 1 Contributed equally. 1877-7821/$ – see front matter ß 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.canep.2013.04.012
metabolism pathways may contribute to the inter-individual variations in treatment outcomes [3]. The multidrug resistance gene 1 (ABCB1) is responsible for energy dependent efflux of drugs, resulting in low intracellular levels and is encoded by P-glycoprotein (P-gp). Resistance to many anti-cancer drugs including anthracyclines and taxanes are found to be associated with genetic variations affecting function and expression of ABCB1 [4]. Recent clinical studies on breast cancer have shown that the expression of P-glycoprotein is associated with response to chemotherapy [5,6]. Drug transporters including ABCB1 are members of superfamily of ABC (ATP Binding Cassette) transporters and comprise of eight subfamilies. ABCB1 is located on chromosome 7, spans more than 100 kb and expressed as 4.5 kb mRNA [7,8]. More than 20 variations in the ABCB1 gene have been reported until now [9], out of which most commonly studied are 1236C>T (exon 12, rs1128503), 2677G>T/A (exon 21, rs2032582) and 3435C>T (exon 26, rs1045642). The SNPs 1236C>T (Gly412Gly) and 3435C>T (Ile1144Ile) are synonymous while 2677G>T/A results in an amino-acid change from Ala at codon 893 to Ser/Thr. The
P. Chaturvedi et al. / Cancer Epidemiology 37 (2013) 754–761
2677G>T/A and 3435C>T polymorphisms are in linkage disequilibrium [9,10]. Some studies report that 3435C>T plays a role in response to chemotherapy in patients with locally advanced [2] as well as advanced breast cancer [11]. However, other studies do not find any role of the polymorphism in treatment response [12,13]. Similarly, for toxicity, Tsai et al. [14] have shown that patients with ABCB1 2677G/G genotype suffered more from febrile neutropenia than other genotypes. The patients having 3435C/C genotype were more prone to leucopenia. On the contrary, Cizmarikova et al. [12] found no association of 3435C>T with hematologic toxicities in breast cancer patients. Due to such contradicting studies, the present investigation is aimed at finding the role of these polymorphisms in predicting clinical outcomes in terms of response to chemotherapy and grade 2–4 toxicity. Moreover, a meta analysis was also performed to draw overall conclusions. 2. Materials and methods 2.1. Patients and treatment regimen Two hundred and seven breast cancer patients treated at the Departments of Endocrine & Breast Surgery; and Radiotherapy, Sanjay Gandhi Post Graduate Institute of Medical Sciences (SGPGIMS), Lucknow, India were recruited in this study. Patients who were treated with FAC/FEC (Fluorouracil, Epirubicin/doxorubicin and Cyclophosphamide) chemotherapy regimens were included in this study. Written informed consent, after the approval of the ethical committee of the institute was taken. The patients were graded according to the TNM staging and treated as per standard institutional protocols, which involved surgery, radiation therapy, chemotherapy and hormonal therapy. Demographic and clinico-pathological parameters of the patients were recorded and are illustrated in Table 1. Of the 207 patients, 100 received NACT and 107 received ACT following surgery. Tumor response was evaluated in patients receiving NACT according to RECIST criteria (Response Evaluation Criteria in Solid Tumors) [15], 3 weeks after three cycles as well as last cycle of chemotherapy. The patients with complete and partial pathological response were categorized as responders while static and progressive disease were categorized as non-responders. Surgery was performed after 3 weeks of last cycle of chemotherapy. According to NCI-CTCAE [16], grade 2–4 toxicity was recorded in 207 patients, in terms of grade 2–4 anemia (hemoglobin < 10 g/ dl), leucopenia (TLC < 3000/mcL) and thrombocytopenia (platelets count < 75,000/mcL) [16]. Records of patients who had dose delay or reduction due to febrile neutropenia were also maintained. 2.2. Genotyping Blood samples were collected in EDTA (ethylene-diaminetetra-acetic acid) vials and genomic DNA was extracted from peripheral blood leukocyte pellet using a modified salting-out method [17]. The quality and quantity of DNA was checked spectrophotometrically using the Nano Drop Analyzer-1000 spectrophotometer (Nano Drop Technologies, Wilmington, DE, USA). The ratio of absorbance at 260 and 280 nm of DNA was between 1.7 and 1.9 and the isolated DNA was stored at 70 8C. Polymerase chain reaction (PCR)-Restriction fragment length polymorphism (RFLP) was used to determine the genotypic frequencies of 1236C>T [10], 2677G>T/A [10] and 3435C>T [18] (representative gel pictures are shown in supplementary Figures S1a–c). Ten percent of the samples from patients including samples of each genotype were re-genotyped by other laboratory personnel. No discrepancy was found after sequencing randomly selected 5% samples.
755
See Figure S1 as supplementary file. Supplementary material related to this article found, in the online version, at http:// dx.doi.org/10.1016/j.canep.2013.04.012. 2.3. Literature search strategy and data extraction for meta analysis Literature search was carried out in PubMed, OVID and Springer, covering all the papers published until December 2012, using the keywords multidrug resistance gene, ABC transporters, breast cancer, pharmacogenetics and toxicity. Reference lists of key studies and reviews were also screened for additional related studies. The criteria used for literature selection were: (a) original papers, (b) exploring the association between the three selected SNPs and chemotherapy response and toxicity, (c) papers with crude Odd’s ratio and 95% confidence interval or sufficient data to calculate overall OR at 95% CI), (d) chemotherapy response evaluation by RECIST criteria. A total of 66 related studies were found by using these research criteria. After screening according to the inclusion criteria, finally 8 publications were included for meta analysis. Information on the following data was collected for each study: first author’s name, publication date, country, ethnicity, number of patients included in the study, clinical stage, treatment protocols, genotyping methods, evaluation criteria and sample origin. 2.4. Statistical analysis Descriptive statistics of patients were presented as mean and standard deviations for continuous measures whereas frequencies and percentages were used for categorical measures. Effective sample sizes were calculated by the Quanto software version 1.2 [19]. Statistical significance of differences in genotype frequencies between patients with different treatment outcomes was estimated by the x2 test. Binary logistic regression was used for all analysis variables to estimate risk as odds ratio (OR) with 95% confidence intervals (95%CIs). All statistical analyses were performed using the SPSS software version 17.0 (SPSS, Chicago, IL, USA) and tests of statistical significance were two-sided. In meta analysis, pooled odd’s ratios and confidence intervals were calculated for overall toxicity and response. Analyses were weighted by trial size. Statistical heterogeneity was measured using the Q statistic (p < 0.10 was considered as significant heterogeneity) [20]. The effect of heterogeneity was also quantified by I2 statistic with the following suggested cut off points: I2 = 0– 25%, no heterogeneity; I2 = 25–50%, moderate heterogeneity and I2 = 75–100% extreme heterogeneity [21]. Fixed effects model was used when no heterogeneity was found, otherwise random effects model was used. Publication bias was investigated with funnel plot, in which the standard error of log OR of each study was plotted against its OR. Funnel plot asymmetry was further assessed by the method of Egger’s linear regression test [22]. 3. Results 3.1. Genotypes and treatment response According to the RECIST criteria, response assessment was made in one hundred patients who were given NACT, and it was observed that 61 (61%) patients were responders and 39 (39%) patients were non-responders (Table 2). For 1236C>T polymorphism, the CT genotype was significantly associated with adverse response to chemotherapy [OR = 5.17(1.3–20.02), P = 0.018]. We also observed significant results when dominant model was applied to the above polymorphism [OR = 4.63(1.25–17.0), P = 0.021] (Table 2). However no association was seen at the allelic level.
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Table 1 The clinico-pathological parameters among patients with different treatment response and toxicity. Indices
Non-responders n(%) = 39(39)
Age (years) 15 (38.5) 45 46–55 13 (33.3) >55 11 (28.2) Menopausal status at diagnosis Premenopausal 12 (30.8) Perimenopausal 0 (0.0) 27 (69.2) Postmenopausal Unknown 0 (0) Estrogen receptor status Positive 17 (43.6) Negative 21 (53.8) Unknown 1 (2.6) Progesterone receptor status 14 (35.9) Positive Negative 24 (61.5) Unknown 1 (2.6) Her 2 neu status Negative 14 (35.9) Positive 22 (56.4) 3 (7.7) Unknown TNM stage at diagnosis (clinical) 0 0 (0) I 0 (0) II 1 (2.6) III 33 (84.6) IV 5 (12.8) Unknown 0 (0) Lymph node status N0 4 (10.3) N1 17 (43.6) N2 15 (38.5) N3 3 (7.7) Unknown 0 (0) Tumor grade Grade I 2 (5.1) Grade II 20 (51.3) Grade III 17 (43.6) Hormone receptor status Negative 20 (51.3) Positive 18 (46.2) Unknown 1 (2.6) *
Responders n(%) = 61 (61)
P value
Toxicity n = 107 (51.7)
No-toxicity n = 100 (48.3)
P value
23 (37.7) 23 (37.7) 15 (24.6)
0.883
43 (40.2) 33 (30.8) 31 (29.0)
38 (38.0) 41 (41.0) 21 (21.0)
0.239
40 3 64 0
39 5 55 1
25 4 32 0
(41.0) (6.6) (52.5) (0)
0.113
(2.8) (2.8) (59.8) (0.0)
(39.0) (5.0) (55.0) (1.0)
0.581
34 (55.7) 27 (44.3) 0 (0.0)
0.258
57 (53.3) 49 (45.8) 1 (0.9)
46 (46.0) 54 (54.0) 0 (0.0)
0.336
26 (42.6) 35 (57.4) 0 (0.0)
0.386
46 (43.0) 58 (54.2) 3 (2.8)
45 (45.0) 55 (55.0) 0 (0.0)
0.24
26 (42.6) 33 (54.1) 2 (3.3)
0.544
56 (52.3) 48 (44.9) 3 (2.8)
62 (62.0) 31 (31.0) 7 (7.0)
0.07
0 6 39 51 11 0
1 6 40 47 1 5
0 0 0 52 6 3
(0) (0) (85.2) (9.8) (4.9)
6 29 18 5 3
(9.8) (47.5) (29.5) (8.2) (4.9)
0.299
(0) (5.6) (36.4) (47.7) (10.3) (0)
(1.0) (6.0) (40.0) (47.0) (1.0) (5.0)
0.014*
0.629
34 (31.8) 45 (42.1) 24 (22.4) 4 (3.7) 0(0)
38(38.0) 38 (38.0) 15 (15.0) 4 (4.0) 5 (5.0)
0.105
1 (1.6) 34 (55.7) 26 (42.6)
0.589
6 (5.6) 64 (59.8) 37 (34.6)
2 (2.0) 60 (60.0) 38 (38.0)
0.385
26 (42.6) 35 (57.4) 0 (0)
0.284
46 (43.0) 60 (56.1) 1 (0.9)
48 (48.0) 52 (52.0) 0 (0)
0.502
Significant P < 0.05, x2 test. The significant values are shown in bold.
Table 2 Genotypic and allelic frequencies of MDR1 1236C>T, 2677G>T/A and 3435C>T among non responders versus responders. Genotype MDR1 1236C>T CC CT TT Dominant model Alleles C T * MDR1 2677G>T/A Alleles G T A MDR1 3435C>T CC CT TT Dominant model Alleles C T *
Non responders n = 39 (39%)
Responders n = 61 (61%)
OR (95%CI)
P
3 21 15 36
17 23 21 44
(27.9) (37.7) (34.4) (72.1)
Reference 5.17 (1.3–20.2) 4.04 (1.0–16.3) 4.63 (1.25–17.0)
0.018 0.049 0.021
27 (34.6) 51 (65.4)
57 (46.7) 65 (53.3)
Reference 1.65 (0.92–2.97)
0.092
18 (23.1) 56 (71.8) 4 (5.1)
35 (28.7) 83 (68.0) 4 (3.3)
Reference 1.31 (0.67–2.54) 1.94 (0.43–8.69)
0.421 0.384
1 19 19 38
9 30 22 52
Reference 5.70 (0.66–48.66) 7.77 (0.90–67.07) 6.57 (0.79–54.13)
0.11 0.06 0.08
Reference 1.76 (0.94–3.26)
0. 07
(7.7) (53.8) (38.5) (92.3)
(2.6) (48.7) (48.7) (97.4)
21 (26.9) 57 (73.1)
(14.8) (49.2) (36.1) (85.2)
48 (39.3) 74 (60.7)
Genotype frequencies are not shown because there was 0 frequency in wild genotype in case of non-responder. The significant values are shown in bold.
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Table 3 Genotypic and allelic frequencies of MDR1 1236C>T, 2677G>T/A and 3435C>T among patients with grade 2–4 toxicity versus no grade 2–4 toxicity. Genotype MDR1 1236C>T CC CT TT * Recessive model (TT vs CC + CT) Alleles C T MDR1 2677G>T/A GG GT TT GA AT AA Dominant model Alleles G T A MDR1 3435C>T CC CT TT Dominant model Alleles C T *
Toxicity n = 107 (51.7%) 15 48 44 63
(14.0) (44.9) (41.1) (58.9)
78 (36.4) 136 (63.6) 9 58 35 1 4 0 98
(8.4) (54.2) (32.7) (0.9) (3.7) (0) (91.6)
77 (36.0) 132 (61.7) 5 (2.3) 13 56 38 94
(12.1) (52.3) (35.5) (87.9)
82 (38.3) 132 (61.7)
No toxicity n = 100 (48.3%)
OR (95%CI)
P
(19.0) (54.0) (27.0) (73.0)
Reference 1.12 (0.51–2.45) 2.06 (0.90–4.73) 1.88 (1.05–3.39)
0.766 0.087 0.033
92 (46.0) 108 (54.0)
Reference 1.48 (1.00–2.20)
0.049
Reference 0.80 (0.26–2.42) 0.64 (0.20–2.01) 0.66 (0.03–12.84) 0.29 (0.06–1.41) – 0.69 (0.23–2.02)
0.700 0.453 0.788 0.128 – 0.506
Reference 0.81 (0.53–1.22) 0.39 (0.12–1.22)
0.321 0.107
(16.0) (43.0) (41.0) (84.0)
Reference 1.60 (0.69–3.68) 1.14 (0.48–2.68) 1.37 (0.62–3.03)
0.267 0.763 0.426
75 (37.5) 125 (62.5)
Reference 0.96 (0.64–1.43)
0.864
19 54 27 73
6 48 36 1 9 0 94
(6.0) (48.0) (36.0) (1.0) (9.0) (0) (94.0)
61 (30.5) 129 (64.5) 10 (5.0) 16 43 41 84
Dominant model not shown as P value not significant. The significant values are shown in bold.
For ABCB1 2677G>T/A and 3435C>T, no association at the genotype or at the allele level was found. Similarly, no association was observed on applying dominant model in 2677G>T/A and 3435C>T polymorphisms (Table 2). 3.2. Genotypes and grade 2–4 toxicity On the basis of anemia, leucopenia and thrombocytopenia, grade 2–4 toxicity was assessed in 207 patients. It was observed that 107 (51.7%) patients suffered from grade 2–4 toxicity while 100 (48.3%) had no grade 2–4 toxicity (Table 3). For 1236C>T polymorphism, in recessive model (TT vs CC + TT) we found significant association with toxicity [OR 1.88(1.05–3.39), P = 0.033] and at allele level the T allele was associated with grade 2–4 toxicity [OR = 1.48(1.00–2.20), P = 0.049]. For 2677G>T/ A and 3435C>T, no association of genotype was seen with drug related grade 2–4 toxicity (Table 3). Anemia: Association of genotypes was also assessed in 207 patients with grade 2–4 anemia. Out of 207 patients, 95 (45.9%) patients suffered from grade 2–4 anemia while 112 (54.1%) had no grade 2–4 anemia (Table 4). In the recessive model the TT genotype of 1236C>T was significantly associated with grade 2–4 anemia [OR 1.90(1.06–3.39), P = 0.030]. At the allele level the T allele also showed association [OR 1.56(1.06–0.23), P = 0.028]. No association of the ABCB1 2677G>T/A and 3435C>T polymorphism was observed with drug induced grade 2–4 anemia. (Table 4). Leucopenia: In breast cancer patients from grade 2–4 toxicity group, 51 (41.6%) suffered from grade 2–4 leucopenia while 156 (75.4%) had grade 1 or no leucopenia (Table 5). For 1236C>T polymorphism, the variant genotype [OR = 0.87(0.34–2.23), P = 0.779]; or allele [OR = 0.94(0.59–1.48), P = 0.796] was not associated with grade 2–4 leucopenia. The dominant model also showed no association with grade 2–4 leucopenia [OR = 0.89(0.38– 2.05), P = 0.786]. For 2677G>T/A and 3435C>T also, no association of genotypes and alleles was seen with grade 2–4 leucopenia (Table 5).
Thrombocytopenia: Out of 207 patients recruited in the study, only 5 (2.4%) patients suffered from grade 2–4 thrombocytopenia. Due to less number of patients in this group, statistical analysis could not be performed. Dose delay/reduction due to febrile neutropenia: We also assessed chemotherapeutic response in terms of dose delay and dose reduction. This was observed due to febrile neutropenia i.e. any neutropenia with fever in breast cancer patients. Out of 207 patients, dose delay/reduction was observed in 30 (14.5%) patients (Table 6). For ABCB1 1236C>T, in recessive model (TT vs CT + CC) significant association was found with dose delay [OR 2.53(1.15– 5.55), P = 0.020] (Table 6). For 3435C>T and 2677G>T/A polymorphisms, no significance was observed at the genotype or the allele level among patients with dose delay/reduction due to febrile neutropenia.
4. Meta analysis 4.1. Meta-analysis and treatment response For ABCB1 1236C>T, there were two eligible studies by Zhang et al. [13] and Ji et al. [26]. We combined the results of our study to calculate pooled OR which included 373 cases but did not found any significant overall association [OR 1.77(1.01– 3.10), P = 0.05] (Fig. 1). Similarly, for 2677G>T/A, two studies were found eligible i.e. by Chang et al. [23] and Ji et al. [26] and on combining the results of these studies with our study, overall no association was found with response to chemotherapy OR 1.39(0.68–2.82), P = 0.36 (Fig. 1). For 3435C>T, 8 studies were found eligible and 608 cases (including 100 patients from our study) were included. The results indicated heterogeneity, hence random model was used for meta analysis [Q = 15.33, df = 7, I2 = 54.34%, Phet = 0.03]. Overall no association of this SNP was found with response to chemotherapy [OR = 1.13(0.58–0.37), P = 0.71] (Fig. 1).
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Table 4 Allelic and genotypic frequencies of MDR1 1236C>T, 2677G>T/A and 3435C>T among patients with grade 2–4 anemia versus no grade 2–4 anemia. Anemia n = 95 (45.9%)
Genotype MDR1 1236C>T CC CT TT * Recessive model (TT vs. CT + CC) Alleles C T MDR1 2677G>T/A GG GT TT GA AT AA Dominant model Alleles G T A MDR1 3435C>T CC CT TT Dominant model Alleles C T *
12 43 40 55
(12.6) (45.3) (42.1) (57.5)
67 (35.3) 123 (64.7) 8 52 30 1 4 0 87
(8.4) (54.7) (31.6) (1.1) (4.2) (0) (91.6)
69 (36.3) 116 (61.1) 5 (2.6) 10 52 33 85
(10.5) (54.7) (34.7) (89.5)
72 (37.9) 118 (62.1)
No anemia n = 112 (54.1%)
OR (95%CI)
P
Reference 1.33 (0.59–2.99) 2.36 (1.01–5.50) 1.90 (1.06–3.39)
0.481 0.046 0.03
103 (46.0) 121 (54.0)
Reference 1.56 (1.05–0.232)
0.028
7 54 41 1 9 0 105
Reference 0.84 (0.28–2.49) 0.64 (0.20–1.95) 0.87 (0.04–16.74) 0.38 (0.08–1.84) – 0.72 (0.25–2.07)
0.757 0.435 0.929 0.234 – 0.550
Reference 0.80 (0.52–1.21) 0.50 (0.16–1.53)
0.290 0.227
(17.0) (42.0) (41.1) (83.0)
Reference 2.10 (0.88–4.97) 1.36 (0.56–3.30) 1.73 (0.76–3.94)
0.091 0.494 0.187
85 (37.9) 139 (62.1)
Reference 1.00 (0.67–1.49)
0.991
22 59 31 81
(19.6) (52.7) (27.7) (72.3)
(6.3) (48.2) (36.6) (0.9) (8.0) (0) (93.8)
69 (30.8) 145 (64.7) 10 (4.5) 19 47 46 93
Dominant model not shown as P value not significant. The significant values are shown in bold.
4.2. Meta-analysis and grade 2–4 toxicity For ABCB1 1236C>T, 4 studies were found eligible which included 534 cases. The meta analysis indicated heterogeneity, hence random model was used [Q = 3.04, df = 2, I2 = 34.18, Phet = 0.22]. Overall, there was no association of this SNP with grade 2–4 toxicity [OR = 1.14(0.71–1.82), P = 0.58] (Fig. 2). Furthermore, for 2677G>T/A, two studies were found eligible i.e. by
Tsai et al. [14] and by Ji et al. [26]. Combining these studies with our study, we derived a pooled OR which included 418 cases. Overall there was no association of this SNP with drug related toxicity [OR = 0.71(0.38–2.68), P = 1.34] (Fig. 2). For 3435C>T, five studies were found eligible including our study which consisted of 491 cases of which 241 patients suffered from grade 2–4 toxicity. The results showed heterogeneity hence random model was used for meta analysis [Q = 8.22, df = 3, I2 = 63.54, Phet = 0.04]. The
Table 5 Allelic and genotypic frequencies of MDR1 1236C>T, 2677G>T/A and 3435C>T among patients with grade 2–4 Leucopenia versus no grade 2–4 leucopenia. Genotype MDR1 1236C>T CC vCT TT * Recessive model (TT vs. CC + TT) Alleles C T MDR1 2677G>T/A GG GT TT GA AT AA Dominant model Alleles G T A MDR1 3435C>T CC CT TT Dominant model Alleles C T *
Leucopenia, n = 51 (24.6%)
No leucopenia, n = 156 (75.4%)
OR (95%CI)
P
9 25 17 34
25 77 54 102
(16.0) (49.4) (34.6) (65.4)
Reference 0.90 (0.37–2.18) 0.87 (0.34–2.23) 0.94 (0.48–1.84)
0.819 0.779 0.86
43 (42.2) 59 (57.8)
127 (40.7) 185 (59.3)
Reference 0.94 (0.59–1.48)
0.796
5 29 14 1 2 0 46
10 77 57 1 11 0 146
Reference 0.75 (0.23–2.39) 0.49 (0.14–1.66) 2.00 (0.10–39.07) 0.36 (0.05–2.31)
0.631 0.254 0.648 0.284
0.63 (0.20–1.93)
0.420
(17.6) (49.0) (33.3) (66.7)
(9.8) (56.9) (27.5) (2.0) (3.9) (0) (90.2)
(6.4) (49.4) (36.5) (0.6) (7.1) (0) (93.6)
40 (39.2) 59 (57.8) 3 (2.9)
98 (31.4) 202 (64.7) 12 (3.8)
Reference 0.71 (0.44–1.14) 0.61 (0.16–2.28)
0.161 0.466
7 27 17 44
22 72 62 134
Reference 1.17 (0.45–3.07) 0.86 (0.31–2.35) 1.03 (0.41–2.58)
0.737 0.772 0.946
Reference 0.881 (0.55–1.39)
0.586
(13.7) (52.9) (33.3) (86.3)
41 (40.2) 61 (59.8)
P value not significant for both dominant and recessive models.
(14.1) (46.2) (39.7) (85.9)
116 (37.2) 196 (62.8)
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Table 6 Allelic and genotypic frequencies of MDR1 1236C>T, 2677G>T/A and 3435C>T among patients with dose delay/reduction due to neutropenia. Genotype MDR1 1236C>T CC CT TT Recessive model (TT vs. CT + CC)* Alleles C T MDR1 2677G>T/A GG GT TT GA AT AA Dominant model Alleles G T A MDR1 3435C>T CC CT TT Dominant model Allele C T *
Dose delay/reduction, n = 30 (14.5%)
No dose delay/reduction, n = 177 (85.5%)
OR (95%CI)
P
5 9 16 14
29 93 55 122
(16.4) (52.5) (31.1) (68.9)
Reference 0.56 (0.17–1.80) 1.68 (0.56–5.07) 2.53 (1.15–5.55)
0.333 0.351 0.02
19 (31.7) 41 (68.3)
151 (42.7) 203 (57.3)
Reference 1.60 (0.89–2.87)
0.112
3 14 12 0 1 0 27
12 92 59 2 12 0 165
(6.8) (52.0) (33.3) (1.1) (6.8) (0) (93.2)
Reference 0.60 (0.15–2.43) 0.81 (0.19–3.33) 0.00 0.33 (0.03–3.67) – 0.65 (0.17–2.47)
0.482 0.774 0.999 0.370 – 0.532
20 (33.3) 39 (65.0) 1 (1.7)
118 (33.3) 222 (62.7) 14 (4.0)
Reference 1.03 (0.57–1.85) 0.42 (0.05–3.38)
0.904 0.416
3 11 16 27
26 88 63 151
(14.7) (49.7) (35.6) (85.3)
Reference 1.08 (0.28–4.17) 2.20 (0.59–8.19) 1.55 (0.43–5.48)
0.907 0.240 0.497
140 (39.5) 214 (60.5)
Reference 1.65 (0.90–3.01)
0.100
(16.7) (30.0) (53.3) (46.7)
(10.0) (46.7) (40.0) (0.0) (3.3) (0) (90.0)
(10.0) (36.7) (53.3) (90.0)
17 (28.3) 43 (71.7)
Dominant model not shown as P value not significant. The significant values are shown in bold.
overall analysis did not show any association of this SNP with drug related toxicity [OR = 1.08(0.58–2.02), P = 0.81] (Fig. 2).
compared for both fixed and random models and there was not much difference in the results. Sensitivity analysis showed that the results of meta analysis were quite stable and consistent.
4.3. Sensitivity analysis 4.4. Bias diagnostics The sensitivity of meta analysis was checked by one study removed and see whether there was an effect in the overall OR or not. On removing one study the combined OR did not change much [OR = 0.84(0.59–1.18), P = 0.32]. The combined OR was also
For estimating publication bias, Begg’s funnel plot and egger’s test were conducted. The funnel plot shape for the pooled analysis of toxicity studies did not reveal any asymmetry (Supplementary
Fig. 1. Forest plots for the association of ABCB1 3435C>T, 1236C>T and 2677G>T/A polymorphism with response to chemotherapy in breast cancer patients.
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Fig. 2. Forest plots for the association of ABCB1 1236C>T, 3435C>T and 2677G>T/A polymorphism with drug induced toxicity in breast cancer patients.
Fig S2). The Egger’s regression intercept was 1.21, t value was 0.52, two sided P = 0.61. The statistical tests did not reveal any publication bias. See Figure S2 as supplementary file. Supplementary material related to this article found, in the online version, at http:// dx.doi.org/10.1016/j.canep.2013.04.012. 5. Discussion ln our study the ABCB1 1236C>T SNP was significantly associated with treatment outcomes in terms of response to chemotherapy and toxicity, while the other two SNPs 2677G>T/A and 3435C>T did not show any association with clinical response. Cancer treatment involves multi-modality protocol, out of which multidrug resistance is one of the critical phenomena that should be looked into. Polymorphisms in drug transporters which account for inherited variability, contribute significantly to multidrug resistance. In our study, we evaluated treatment response in one hundred patients and found that 1236C>T was significantly associated with response to chemotherapy. Till date there are only two reports published, suggesting no association of 1236C>T with response to chemotherapy in breast cancer patients of Chinese population [13,26]. We also studied the association of 2677G>T/A which is a triallelic SNP but could not find any association of this polymorphism with treatment response. Chang et al. [23] examined 2677G>T/A in 108 patients and found that the genotype was not correlated with response to chemotherapy. Ji et al. [26] also evaluated chemotherapy response in 153 patients but did not find any association of 26777G>T/A with response. Among the three SNPs, 3435C>T is the most studied ABCB1 polymorphism. In a report by Kafka et al., authors have demonstrated that 3435C>T is associated with pathological response to NACT in advanced breast cancer patients [11]. However, in our study we could not find any association of 3435C>T with response to NACT. George et al. [24] and Rodrigues et al. [2] did not find any association between 3435C>T polymorphisms and response to chemotherapy which is in agreement with our study. A study by Cizmarikova et al. [12] indicated that the C allele in 3435C>T significantly increased the response rate to chemotherapy. In view of contradicting reports,
we performed a meta analysis, which showed that there was no overall association of 3435C>T polymorphism with treatment response. On evaluating 207 patients who suffered from grade 2–4 toxicity on the basis of anemia, leucopenia and thrombocytopenia, we found that one of the three polymorphisms (1236C>T) studied showed significant association. For 1236C>T, there are three reports till date which correlate genotype with breast cancer chemo-toxicity. These studies demonstrated that 1236C>T genotype was not associated with drug induced toxicity [13,14]. Similarly, studies in 2677G>T/A polymorphism could not find any association with toxicity. However, 3435C>T polymorphism was associated with toxicity in terms of leucopenia [14]. In a study by Cizmarikova et al. [12], no association of 3435C>T polymorphism was found with hematological toxicities. However in our study, we find significant association of 1236C>T with chemo-toxicity. Dose delay/reduction due to febrile neutropenia is also a measure of toxicity, hence we also evaluated the association of ABCB1 polymorphisms with dose delay/reduction. Significant association with dose delay/reduction was seen in the recessive model for 1236C>T. To draw a definitive conclusion, we performed a meta analysis of the previous studies which revealed no overall association of ABCB1 1236C>T, 2677G>T/A and 3435C>T polymorphisms with grade 2–4 chemo-toxicity though there was a trend toward association for 1236C>T. Association between ABCB1 1236C>T polymorphism and treatment outcomes in terms of response to chemotherapy in our study may be due to the fact that the presence of variant allele in the ABCB1 gene may lead to a lower expression of P-gp, which further results in accumulation of drugs inside the cell, thus altering the distribution profile of the chemotherapeutic drugs inside cells. Consequently this may lead to toxicity, as we found association of 1236C>T polymorphism with drug related grade 2– 4 toxicity. The 1236C>T polymorphism was associated with dose delay and this dose delay may cause poor response in patients. In meta analysis also, there was trend toward association for 1236C>T with treatment response but the other two SNPs did not show any association, Therefore, ABCB1 polymorphisms do exert significant effects on breast cancer chemotherapy responses. However, it should be kept in mind that not a single gene and its genetic polymorphisms alone are sufficient to determine the
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response to chemotherapy and chemo-toxicity. Other factors that regulate the enzymatic modification of drugs or drug targets, and alternations in molecular pathways regulating cellular response to drug induced toxicity also participate in determining the effect of drugs [25]. In near future, more studies with a pharmacogenetic approach involving whole drug metabolism, transport and drug targets are expected to ascertain the role of genetic variants in predicting treatment outcomes. In conclusion, ABCB1 genetic variations especially 1236C>T showed association with poor response to anthracycline based chemotherapy, which may be due to dose reduction or delay resulting from the toxicity caused by higher accumulation of drug inside the cell in breast cancer patients. However, this observation needs to be validated in larger cohort before clinical applications. Conflicts of interest The authors declare that there are no conflicts of interest. Acknowledgements We are thankful to Indian Council of Medical Research and Department of Science and Technology for financial assistance to carry out this work. References [1] Evans WE, Relling MV. Pharmacogenomics: translating functional genomics into rational therapeutics. Science 1999;286:487–91. [2] Rodrigues FF, Santos RE, Melo MB, Silva MA, Oliveira AL, Rozenowicz RL, et al. Correlation of polymorphism C3435T of the MDR-1 gene and the response of primary chemotherapy in women with locally advanced breast cancer. Genet Mol Res 2008;7(1):177–83. [3] Evans WE, McLeod HL. Pharmacogenomics – drug disposition, drug targets, and side effects. N Engl J Med 2003;348:538–49. [4] Gottesman MM, Fojo T, Bates SE. Multidrug resistance in cancer: role of ATP dependent transporters. Nat Rev Cancer 2002;2:48–58. [5] Burger H, Foekens JA, Look MP, Gelder ME, Klijn JG, Wiemer EA, et al. RNA expression of breast cancer resistance protein, lung resistance-related protein, multidrug resistance-associated proteins 1 and 2, and multidrug resistance gene 1 in breast cancer: correlation with chemotherapeutic response. Clin Cancer Res 2003;9:827–36. [6] Mutoh K, Tsukahara S, Mitsuhashi J, Katayama K, Sugimoto Y. Estrogen mediated post transcriptional down-regulation of P-glycoprotein in MDR1transduced human breast cancer cells. Cancer Sci 2006;97:1198–204. [7] Chen CJ, Clark D, Ueda K, Pastan I, Gottesman MM, Roninson IB. Genomic organization of the human multidrug resistance (MDR1) gene and origin of Pglycoproteins. J Biol Chem 1990;265:506–14.
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[8] Fojo A, Lebo R, Shimizu N, Chin JE, Roninson IB, Merlino GT, et al. Localization of multidrug resistance-associated DNA sequences to human chromosome 7. Somat Cell Mol Genet 1986;12:415–20. [9] Kim RB, Leake BF, Choo EF, Dresser GK, Kubba SV, Schwarz UI, et al. Identification of functionally variant MDR1 alleles among European Americans and African Americans. Clin Pharmacol Ther 2001;70:189–99. [10] Cascorbi I, Gerloff T, Johne A, Meisel C, Hoffmeyer S, Schwab M, et al. Frequency of single nucleotide polymorphisms in the P-glycoprotein drug transporter MDR gene in white subjects. Clin Pharmacol Ther 2001;69:169–74. [11] Kafka A, Sauer G, Jaeger C, Grundmann R, Kreinberg R, Zeillinger R, et al. Polymorphism C3435T of the MDR-1 gene predicts response to preoperative chemotherapy in locally advanced breast cancer. Int J Oncol 2003;22:1117– 21. [12] Cizmarikova M, Wagnerova M, Schonova L, Habalova V, Kohut A, Linkova M, et al. MDR1 (C3435T) polymorphism: relation to the risk of breast cancer and therapeutic outcome. Pharmacogenomics J 2010;10:62–9. [13] Zhang BL, Sun T, Zhang BN, Zheng S, Lu N, Xu BH, et al. Polymorphisms of GSTP1 is associated with differences of chemotherapy response and toxicity in breast cancer. Chin Med J (Engl) 2011;124:199–204. [14] Tsai SM, Lin CY, Wu SH, Hou LA, Ma H, Tsai LY, et al. Side effects after docetaxel treatment in Taiwanese breast cancer patients with CYP3A4, CYP3A5, and ABCB1 gene polymorphisms. Clin Chim Acta 2009;404(2):160–5. [15] Therasse P, Arbuck SG, Eisenhauer EA, Wanders J, Kaplan RS, Rubinstein L, et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst 2000;92(3):205–16. [16] Cancer Therapy Evaluation Program. Common terminology criteria for adverse events, version 3.0. DCTD, NCI, NIH, DHHS; 2003 , http://ctep.cancer.gov [accessed 13.02.13]. [17] Miller SA, Dykes DD, Polesky HF. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res 1988;16(3):1215. [18] Hamdy SI, Hiratsuka M, Narahara K, Endo N, Enany M, Moursi N, et al. Genotype and allele frequencies of TPMT, NAT2, GST, SULT1A1 and MDR-1 in the Egyptian population. Br J Clin Pharmacol 2003;55:560–9. [19] Gauderman WJ. Sample size requirements for matched case–control studies of gene–environment interaction. Stat Med 2002;21(1):35–50. [20] Cochran W. The combination of estimates from different experiments. Biometrics 1954;10:101–29. [21] Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med 2002;21(11):1539–58. [22] Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997;315(7109):629–34. [23] Chang H, Rha SY, Jeung HC, Im CK, Ahn JB, Kwon WS, et al. Association of the ABCB1 gene polymorphisms 2677G>T/A and 3435C>T with clinical outcomes of paclitaxel monotherapy in metastatic breast cancer patients. Ann Oncol 2009;20:272–7. [24] George J, Dharanipragada K, Krishnamachari S, Chandrasekaran A, Sam SS, Sunder E. A single-nucleotide polymorphism in the MDR1 gene as a predictor of response to neoadjuvant chemotherapy in breast cancer. Clin Breast Cancer 2009;9:161–5. [25] Leonessa F, Clarke R. ATP binding cassette transporters and drug resistance in breast cancer. Endocr Relat Cancer 2003;10:43–73. [26] Ji M, Tang J, Zhao J, Xu B, Qin J, Lu J. Polymorphisms in genes involved in drug detoxification and clinical outcomes of anthracycline-based neoadjuvant chemotherapy in Chinese Han breast cancer patients. Cancer Biol Ther 2012;13(5):264–71.
Contents lists available at SciVerse ScienceDirect
Cancer Epidemiology The International Journal of Cancer Epidemiology, Detection, and Prevention journal homepage: www.cancerepidemiology.net
Influence of ABCB1 genetic variants in breast cancer treatment outcomes P. Chaturvedi a,1, S. Tulsyan a,1, G. Agarwal b, P. Lal c, S. Agarwal c, R.D. Mittal d, B. Mittal a,* a
Department of Genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India Department of Endocrine & Breast Surgery, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India c Department of Radiotherapy, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India d Department of Urology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 11 January 2013 Received in revised form 20 March 2013 Accepted 26 April 2013 Available online 23 May 2013
Background: Transportation of anticancer drugs such as anthracyclines across the membrane is regulated by P-glycoprotein encoded by the human multidrug resistance gene 1 (ABCB1). Polymorphisms in the ABCB1 gene (1236C > T, 2677G > T/A, 3435C > T) have been found to be associated with intrinsic and acquired cross resistance to these anticancer drugs. Therefore, the aim of this study is to evaluate the influence of ABCB1 gene polymorphisms in breast cancer treatment outcomes in terms of response and toxicity. Method: Response to neo-adjuvant chemotherapy was evaluated in 100 patients while grade 2–4 toxicity was followed in 207 patients, who had undergone FEC/FAC chemotherapy. Genotyping for ABCB1 polymorphisms was done by PCR-RFLP. Chi square and logistic regression analyses were used to calculate Odd’s ratio using SPSS ver 17.0. A meta analysis was also performed using Comprehensive Meta Analysis Ver 2. Results: In response evaluation, 1236C > T polymorphism was significantly associated with treatment response for CT genotype [OR = 5.17(1.3–20.2), P = 0.018] and in dominant model (CC vs CT + TT) [OR = 4.63(1.25–17.0), P = 0.021]. In the toxicity group, the T allele of 1236C>T was associated with grade 2–4 tocxicity [OR 1.48(1.00–2.20), P = 0.049] and the association was also significant in the recessive model [OR 1.88(1.05–3.39), P = 0.033]. For other two SNPs 2677G>T/A and 3435C>T no association was seen with either treatment response or grade 2–4 toxicity. In meta analysis, no overall association was found. Conclusion: In our study, significant association was seen for ABCB1 1236C>T polymorphism with treatment response. The meta analysis did not show overall association with treatment outcomes. ß 2013 Elsevier Ltd. All rights reserved.
Keywords: Breast cancer Multi drug resistance gene Polymorphism Treatment outcomes Response Toxicity
1. Introduction Breast cancer patients are treated with chemotherapy depending upon the stage, e.g. locally advanced breast cancers are treated with neo-adjuvant chemotherapy (NACT) while early breast cancers are treated with primary surgery followed by adjuvant chemotherapy (ACT). Treatment strategies include chemotherapy – anthracyclines (epirubicin/doxorubicin), cytotoxics (cyclophosphamide, paclitaxel, docetaxel), hormone therapy anti-estrogens (tamoxifen) and aromatase inhibitors (exemestane, anastrozole, letrozole). A significant heterogeneity is observed in the response and toxicity to chemotherapeutic agents [1,2]. Genetic differences in drug transporters, enzymes of primary and secondary
* Corresponding author at: Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow 226 014, India. Tel.: +91 522 249 4322; fax: +91 522 2668973. E-mail addresses: [email protected], [email protected] (B. Mittal). 1 Contributed equally. 1877-7821/$ – see front matter ß 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.canep.2013.04.012
metabolism pathways may contribute to the inter-individual variations in treatment outcomes [3]. The multidrug resistance gene 1 (ABCB1) is responsible for energy dependent efflux of drugs, resulting in low intracellular levels and is encoded by P-glycoprotein (P-gp). Resistance to many anti-cancer drugs including anthracyclines and taxanes are found to be associated with genetic variations affecting function and expression of ABCB1 [4]. Recent clinical studies on breast cancer have shown that the expression of P-glycoprotein is associated with response to chemotherapy [5,6]. Drug transporters including ABCB1 are members of superfamily of ABC (ATP Binding Cassette) transporters and comprise of eight subfamilies. ABCB1 is located on chromosome 7, spans more than 100 kb and expressed as 4.5 kb mRNA [7,8]. More than 20 variations in the ABCB1 gene have been reported until now [9], out of which most commonly studied are 1236C>T (exon 12, rs1128503), 2677G>T/A (exon 21, rs2032582) and 3435C>T (exon 26, rs1045642). The SNPs 1236C>T (Gly412Gly) and 3435C>T (Ile1144Ile) are synonymous while 2677G>T/A results in an amino-acid change from Ala at codon 893 to Ser/Thr. The
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2677G>T/A and 3435C>T polymorphisms are in linkage disequilibrium [9,10]. Some studies report that 3435C>T plays a role in response to chemotherapy in patients with locally advanced [2] as well as advanced breast cancer [11]. However, other studies do not find any role of the polymorphism in treatment response [12,13]. Similarly, for toxicity, Tsai et al. [14] have shown that patients with ABCB1 2677G/G genotype suffered more from febrile neutropenia than other genotypes. The patients having 3435C/C genotype were more prone to leucopenia. On the contrary, Cizmarikova et al. [12] found no association of 3435C>T with hematologic toxicities in breast cancer patients. Due to such contradicting studies, the present investigation is aimed at finding the role of these polymorphisms in predicting clinical outcomes in terms of response to chemotherapy and grade 2–4 toxicity. Moreover, a meta analysis was also performed to draw overall conclusions. 2. Materials and methods 2.1. Patients and treatment regimen Two hundred and seven breast cancer patients treated at the Departments of Endocrine & Breast Surgery; and Radiotherapy, Sanjay Gandhi Post Graduate Institute of Medical Sciences (SGPGIMS), Lucknow, India were recruited in this study. Patients who were treated with FAC/FEC (Fluorouracil, Epirubicin/doxorubicin and Cyclophosphamide) chemotherapy regimens were included in this study. Written informed consent, after the approval of the ethical committee of the institute was taken. The patients were graded according to the TNM staging and treated as per standard institutional protocols, which involved surgery, radiation therapy, chemotherapy and hormonal therapy. Demographic and clinico-pathological parameters of the patients were recorded and are illustrated in Table 1. Of the 207 patients, 100 received NACT and 107 received ACT following surgery. Tumor response was evaluated in patients receiving NACT according to RECIST criteria (Response Evaluation Criteria in Solid Tumors) [15], 3 weeks after three cycles as well as last cycle of chemotherapy. The patients with complete and partial pathological response were categorized as responders while static and progressive disease were categorized as non-responders. Surgery was performed after 3 weeks of last cycle of chemotherapy. According to NCI-CTCAE [16], grade 2–4 toxicity was recorded in 207 patients, in terms of grade 2–4 anemia (hemoglobin < 10 g/ dl), leucopenia (TLC < 3000/mcL) and thrombocytopenia (platelets count < 75,000/mcL) [16]. Records of patients who had dose delay or reduction due to febrile neutropenia were also maintained. 2.2. Genotyping Blood samples were collected in EDTA (ethylene-diaminetetra-acetic acid) vials and genomic DNA was extracted from peripheral blood leukocyte pellet using a modified salting-out method [17]. The quality and quantity of DNA was checked spectrophotometrically using the Nano Drop Analyzer-1000 spectrophotometer (Nano Drop Technologies, Wilmington, DE, USA). The ratio of absorbance at 260 and 280 nm of DNA was between 1.7 and 1.9 and the isolated DNA was stored at 70 8C. Polymerase chain reaction (PCR)-Restriction fragment length polymorphism (RFLP) was used to determine the genotypic frequencies of 1236C>T [10], 2677G>T/A [10] and 3435C>T [18] (representative gel pictures are shown in supplementary Figures S1a–c). Ten percent of the samples from patients including samples of each genotype were re-genotyped by other laboratory personnel. No discrepancy was found after sequencing randomly selected 5% samples.
755
See Figure S1 as supplementary file. Supplementary material related to this article found, in the online version, at http:// dx.doi.org/10.1016/j.canep.2013.04.012. 2.3. Literature search strategy and data extraction for meta analysis Literature search was carried out in PubMed, OVID and Springer, covering all the papers published until December 2012, using the keywords multidrug resistance gene, ABC transporters, breast cancer, pharmacogenetics and toxicity. Reference lists of key studies and reviews were also screened for additional related studies. The criteria used for literature selection were: (a) original papers, (b) exploring the association between the three selected SNPs and chemotherapy response and toxicity, (c) papers with crude Odd’s ratio and 95% confidence interval or sufficient data to calculate overall OR at 95% CI), (d) chemotherapy response evaluation by RECIST criteria. A total of 66 related studies were found by using these research criteria. After screening according to the inclusion criteria, finally 8 publications were included for meta analysis. Information on the following data was collected for each study: first author’s name, publication date, country, ethnicity, number of patients included in the study, clinical stage, treatment protocols, genotyping methods, evaluation criteria and sample origin. 2.4. Statistical analysis Descriptive statistics of patients were presented as mean and standard deviations for continuous measures whereas frequencies and percentages were used for categorical measures. Effective sample sizes were calculated by the Quanto software version 1.2 [19]. Statistical significance of differences in genotype frequencies between patients with different treatment outcomes was estimated by the x2 test. Binary logistic regression was used for all analysis variables to estimate risk as odds ratio (OR) with 95% confidence intervals (95%CIs). All statistical analyses were performed using the SPSS software version 17.0 (SPSS, Chicago, IL, USA) and tests of statistical significance were two-sided. In meta analysis, pooled odd’s ratios and confidence intervals were calculated for overall toxicity and response. Analyses were weighted by trial size. Statistical heterogeneity was measured using the Q statistic (p < 0.10 was considered as significant heterogeneity) [20]. The effect of heterogeneity was also quantified by I2 statistic with the following suggested cut off points: I2 = 0– 25%, no heterogeneity; I2 = 25–50%, moderate heterogeneity and I2 = 75–100% extreme heterogeneity [21]. Fixed effects model was used when no heterogeneity was found, otherwise random effects model was used. Publication bias was investigated with funnel plot, in which the standard error of log OR of each study was plotted against its OR. Funnel plot asymmetry was further assessed by the method of Egger’s linear regression test [22]. 3. Results 3.1. Genotypes and treatment response According to the RECIST criteria, response assessment was made in one hundred patients who were given NACT, and it was observed that 61 (61%) patients were responders and 39 (39%) patients were non-responders (Table 2). For 1236C>T polymorphism, the CT genotype was significantly associated with adverse response to chemotherapy [OR = 5.17(1.3–20.02), P = 0.018]. We also observed significant results when dominant model was applied to the above polymorphism [OR = 4.63(1.25–17.0), P = 0.021] (Table 2). However no association was seen at the allelic level.
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Table 1 The clinico-pathological parameters among patients with different treatment response and toxicity. Indices
Non-responders n(%) = 39(39)
Age (years) 15 (38.5) 45 46–55 13 (33.3) >55 11 (28.2) Menopausal status at diagnosis Premenopausal 12 (30.8) Perimenopausal 0 (0.0) 27 (69.2) Postmenopausal Unknown 0 (0) Estrogen receptor status Positive 17 (43.6) Negative 21 (53.8) Unknown 1 (2.6) Progesterone receptor status 14 (35.9) Positive Negative 24 (61.5) Unknown 1 (2.6) Her 2 neu status Negative 14 (35.9) Positive 22 (56.4) 3 (7.7) Unknown TNM stage at diagnosis (clinical) 0 0 (0) I 0 (0) II 1 (2.6) III 33 (84.6) IV 5 (12.8) Unknown 0 (0) Lymph node status N0 4 (10.3) N1 17 (43.6) N2 15 (38.5) N3 3 (7.7) Unknown 0 (0) Tumor grade Grade I 2 (5.1) Grade II 20 (51.3) Grade III 17 (43.6) Hormone receptor status Negative 20 (51.3) Positive 18 (46.2) Unknown 1 (2.6) *
Responders n(%) = 61 (61)
P value
Toxicity n = 107 (51.7)
No-toxicity n = 100 (48.3)
P value
23 (37.7) 23 (37.7) 15 (24.6)
0.883
43 (40.2) 33 (30.8) 31 (29.0)
38 (38.0) 41 (41.0) 21 (21.0)
0.239
40 3 64 0
39 5 55 1
25 4 32 0
(41.0) (6.6) (52.5) (0)
0.113
(2.8) (2.8) (59.8) (0.0)
(39.0) (5.0) (55.0) (1.0)
0.581
34 (55.7) 27 (44.3) 0 (0.0)
0.258
57 (53.3) 49 (45.8) 1 (0.9)
46 (46.0) 54 (54.0) 0 (0.0)
0.336
26 (42.6) 35 (57.4) 0 (0.0)
0.386
46 (43.0) 58 (54.2) 3 (2.8)
45 (45.0) 55 (55.0) 0 (0.0)
0.24
26 (42.6) 33 (54.1) 2 (3.3)
0.544
56 (52.3) 48 (44.9) 3 (2.8)
62 (62.0) 31 (31.0) 7 (7.0)
0.07
0 6 39 51 11 0
1 6 40 47 1 5
0 0 0 52 6 3
(0) (0) (85.2) (9.8) (4.9)
6 29 18 5 3
(9.8) (47.5) (29.5) (8.2) (4.9)
0.299
(0) (5.6) (36.4) (47.7) (10.3) (0)
(1.0) (6.0) (40.0) (47.0) (1.0) (5.0)
0.014*
0.629
34 (31.8) 45 (42.1) 24 (22.4) 4 (3.7) 0(0)
38(38.0) 38 (38.0) 15 (15.0) 4 (4.0) 5 (5.0)
0.105
1 (1.6) 34 (55.7) 26 (42.6)
0.589
6 (5.6) 64 (59.8) 37 (34.6)
2 (2.0) 60 (60.0) 38 (38.0)
0.385
26 (42.6) 35 (57.4) 0 (0)
0.284
46 (43.0) 60 (56.1) 1 (0.9)
48 (48.0) 52 (52.0) 0 (0)
0.502
Significant P < 0.05, x2 test. The significant values are shown in bold.
Table 2 Genotypic and allelic frequencies of MDR1 1236C>T, 2677G>T/A and 3435C>T among non responders versus responders. Genotype MDR1 1236C>T CC CT TT Dominant model Alleles C T * MDR1 2677G>T/A Alleles G T A MDR1 3435C>T CC CT TT Dominant model Alleles C T *
Non responders n = 39 (39%)
Responders n = 61 (61%)
OR (95%CI)
P
3 21 15 36
17 23 21 44
(27.9) (37.7) (34.4) (72.1)
Reference 5.17 (1.3–20.2) 4.04 (1.0–16.3) 4.63 (1.25–17.0)
0.018 0.049 0.021
27 (34.6) 51 (65.4)
57 (46.7) 65 (53.3)
Reference 1.65 (0.92–2.97)
0.092
18 (23.1) 56 (71.8) 4 (5.1)
35 (28.7) 83 (68.0) 4 (3.3)
Reference 1.31 (0.67–2.54) 1.94 (0.43–8.69)
0.421 0.384
1 19 19 38
9 30 22 52
Reference 5.70 (0.66–48.66) 7.77 (0.90–67.07) 6.57 (0.79–54.13)
0.11 0.06 0.08
Reference 1.76 (0.94–3.26)
0. 07
(7.7) (53.8) (38.5) (92.3)
(2.6) (48.7) (48.7) (97.4)
21 (26.9) 57 (73.1)
(14.8) (49.2) (36.1) (85.2)
48 (39.3) 74 (60.7)
Genotype frequencies are not shown because there was 0 frequency in wild genotype in case of non-responder. The significant values are shown in bold.
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Table 3 Genotypic and allelic frequencies of MDR1 1236C>T, 2677G>T/A and 3435C>T among patients with grade 2–4 toxicity versus no grade 2–4 toxicity. Genotype MDR1 1236C>T CC CT TT * Recessive model (TT vs CC + CT) Alleles C T MDR1 2677G>T/A GG GT TT GA AT AA Dominant model Alleles G T A MDR1 3435C>T CC CT TT Dominant model Alleles C T *
Toxicity n = 107 (51.7%) 15 48 44 63
(14.0) (44.9) (41.1) (58.9)
78 (36.4) 136 (63.6) 9 58 35 1 4 0 98
(8.4) (54.2) (32.7) (0.9) (3.7) (0) (91.6)
77 (36.0) 132 (61.7) 5 (2.3) 13 56 38 94
(12.1) (52.3) (35.5) (87.9)
82 (38.3) 132 (61.7)
No toxicity n = 100 (48.3%)
OR (95%CI)
P
(19.0) (54.0) (27.0) (73.0)
Reference 1.12 (0.51–2.45) 2.06 (0.90–4.73) 1.88 (1.05–3.39)
0.766 0.087 0.033
92 (46.0) 108 (54.0)
Reference 1.48 (1.00–2.20)
0.049
Reference 0.80 (0.26–2.42) 0.64 (0.20–2.01) 0.66 (0.03–12.84) 0.29 (0.06–1.41) – 0.69 (0.23–2.02)
0.700 0.453 0.788 0.128 – 0.506
Reference 0.81 (0.53–1.22) 0.39 (0.12–1.22)
0.321 0.107
(16.0) (43.0) (41.0) (84.0)
Reference 1.60 (0.69–3.68) 1.14 (0.48–2.68) 1.37 (0.62–3.03)
0.267 0.763 0.426
75 (37.5) 125 (62.5)
Reference 0.96 (0.64–1.43)
0.864
19 54 27 73
6 48 36 1 9 0 94
(6.0) (48.0) (36.0) (1.0) (9.0) (0) (94.0)
61 (30.5) 129 (64.5) 10 (5.0) 16 43 41 84
Dominant model not shown as P value not significant. The significant values are shown in bold.
For ABCB1 2677G>T/A and 3435C>T, no association at the genotype or at the allele level was found. Similarly, no association was observed on applying dominant model in 2677G>T/A and 3435C>T polymorphisms (Table 2). 3.2. Genotypes and grade 2–4 toxicity On the basis of anemia, leucopenia and thrombocytopenia, grade 2–4 toxicity was assessed in 207 patients. It was observed that 107 (51.7%) patients suffered from grade 2–4 toxicity while 100 (48.3%) had no grade 2–4 toxicity (Table 3). For 1236C>T polymorphism, in recessive model (TT vs CC + TT) we found significant association with toxicity [OR 1.88(1.05–3.39), P = 0.033] and at allele level the T allele was associated with grade 2–4 toxicity [OR = 1.48(1.00–2.20), P = 0.049]. For 2677G>T/ A and 3435C>T, no association of genotype was seen with drug related grade 2–4 toxicity (Table 3). Anemia: Association of genotypes was also assessed in 207 patients with grade 2–4 anemia. Out of 207 patients, 95 (45.9%) patients suffered from grade 2–4 anemia while 112 (54.1%) had no grade 2–4 anemia (Table 4). In the recessive model the TT genotype of 1236C>T was significantly associated with grade 2–4 anemia [OR 1.90(1.06–3.39), P = 0.030]. At the allele level the T allele also showed association [OR 1.56(1.06–0.23), P = 0.028]. No association of the ABCB1 2677G>T/A and 3435C>T polymorphism was observed with drug induced grade 2–4 anemia. (Table 4). Leucopenia: In breast cancer patients from grade 2–4 toxicity group, 51 (41.6%) suffered from grade 2–4 leucopenia while 156 (75.4%) had grade 1 or no leucopenia (Table 5). For 1236C>T polymorphism, the variant genotype [OR = 0.87(0.34–2.23), P = 0.779]; or allele [OR = 0.94(0.59–1.48), P = 0.796] was not associated with grade 2–4 leucopenia. The dominant model also showed no association with grade 2–4 leucopenia [OR = 0.89(0.38– 2.05), P = 0.786]. For 2677G>T/A and 3435C>T also, no association of genotypes and alleles was seen with grade 2–4 leucopenia (Table 5).
Thrombocytopenia: Out of 207 patients recruited in the study, only 5 (2.4%) patients suffered from grade 2–4 thrombocytopenia. Due to less number of patients in this group, statistical analysis could not be performed. Dose delay/reduction due to febrile neutropenia: We also assessed chemotherapeutic response in terms of dose delay and dose reduction. This was observed due to febrile neutropenia i.e. any neutropenia with fever in breast cancer patients. Out of 207 patients, dose delay/reduction was observed in 30 (14.5%) patients (Table 6). For ABCB1 1236C>T, in recessive model (TT vs CT + CC) significant association was found with dose delay [OR 2.53(1.15– 5.55), P = 0.020] (Table 6). For 3435C>T and 2677G>T/A polymorphisms, no significance was observed at the genotype or the allele level among patients with dose delay/reduction due to febrile neutropenia.
4. Meta analysis 4.1. Meta-analysis and treatment response For ABCB1 1236C>T, there were two eligible studies by Zhang et al. [13] and Ji et al. [26]. We combined the results of our study to calculate pooled OR which included 373 cases but did not found any significant overall association [OR 1.77(1.01– 3.10), P = 0.05] (Fig. 1). Similarly, for 2677G>T/A, two studies were found eligible i.e. by Chang et al. [23] and Ji et al. [26] and on combining the results of these studies with our study, overall no association was found with response to chemotherapy OR 1.39(0.68–2.82), P = 0.36 (Fig. 1). For 3435C>T, 8 studies were found eligible and 608 cases (including 100 patients from our study) were included. The results indicated heterogeneity, hence random model was used for meta analysis [Q = 15.33, df = 7, I2 = 54.34%, Phet = 0.03]. Overall no association of this SNP was found with response to chemotherapy [OR = 1.13(0.58–0.37), P = 0.71] (Fig. 1).
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Table 4 Allelic and genotypic frequencies of MDR1 1236C>T, 2677G>T/A and 3435C>T among patients with grade 2–4 anemia versus no grade 2–4 anemia. Anemia n = 95 (45.9%)
Genotype MDR1 1236C>T CC CT TT * Recessive model (TT vs. CT + CC) Alleles C T MDR1 2677G>T/A GG GT TT GA AT AA Dominant model Alleles G T A MDR1 3435C>T CC CT TT Dominant model Alleles C T *
12 43 40 55
(12.6) (45.3) (42.1) (57.5)
67 (35.3) 123 (64.7) 8 52 30 1 4 0 87
(8.4) (54.7) (31.6) (1.1) (4.2) (0) (91.6)
69 (36.3) 116 (61.1) 5 (2.6) 10 52 33 85
(10.5) (54.7) (34.7) (89.5)
72 (37.9) 118 (62.1)
No anemia n = 112 (54.1%)
OR (95%CI)
P
Reference 1.33 (0.59–2.99) 2.36 (1.01–5.50) 1.90 (1.06–3.39)
0.481 0.046 0.03
103 (46.0) 121 (54.0)
Reference 1.56 (1.05–0.232)
0.028
7 54 41 1 9 0 105
Reference 0.84 (0.28–2.49) 0.64 (0.20–1.95) 0.87 (0.04–16.74) 0.38 (0.08–1.84) – 0.72 (0.25–2.07)
0.757 0.435 0.929 0.234 – 0.550
Reference 0.80 (0.52–1.21) 0.50 (0.16–1.53)
0.290 0.227
(17.0) (42.0) (41.1) (83.0)
Reference 2.10 (0.88–4.97) 1.36 (0.56–3.30) 1.73 (0.76–3.94)
0.091 0.494 0.187
85 (37.9) 139 (62.1)
Reference 1.00 (0.67–1.49)
0.991
22 59 31 81
(19.6) (52.7) (27.7) (72.3)
(6.3) (48.2) (36.6) (0.9) (8.0) (0) (93.8)
69 (30.8) 145 (64.7) 10 (4.5) 19 47 46 93
Dominant model not shown as P value not significant. The significant values are shown in bold.
4.2. Meta-analysis and grade 2–4 toxicity For ABCB1 1236C>T, 4 studies were found eligible which included 534 cases. The meta analysis indicated heterogeneity, hence random model was used [Q = 3.04, df = 2, I2 = 34.18, Phet = 0.22]. Overall, there was no association of this SNP with grade 2–4 toxicity [OR = 1.14(0.71–1.82), P = 0.58] (Fig. 2). Furthermore, for 2677G>T/A, two studies were found eligible i.e. by
Tsai et al. [14] and by Ji et al. [26]. Combining these studies with our study, we derived a pooled OR which included 418 cases. Overall there was no association of this SNP with drug related toxicity [OR = 0.71(0.38–2.68), P = 1.34] (Fig. 2). For 3435C>T, five studies were found eligible including our study which consisted of 491 cases of which 241 patients suffered from grade 2–4 toxicity. The results showed heterogeneity hence random model was used for meta analysis [Q = 8.22, df = 3, I2 = 63.54, Phet = 0.04]. The
Table 5 Allelic and genotypic frequencies of MDR1 1236C>T, 2677G>T/A and 3435C>T among patients with grade 2–4 Leucopenia versus no grade 2–4 leucopenia. Genotype MDR1 1236C>T CC vCT TT * Recessive model (TT vs. CC + TT) Alleles C T MDR1 2677G>T/A GG GT TT GA AT AA Dominant model Alleles G T A MDR1 3435C>T CC CT TT Dominant model Alleles C T *
Leucopenia, n = 51 (24.6%)
No leucopenia, n = 156 (75.4%)
OR (95%CI)
P
9 25 17 34
25 77 54 102
(16.0) (49.4) (34.6) (65.4)
Reference 0.90 (0.37–2.18) 0.87 (0.34–2.23) 0.94 (0.48–1.84)
0.819 0.779 0.86
43 (42.2) 59 (57.8)
127 (40.7) 185 (59.3)
Reference 0.94 (0.59–1.48)
0.796
5 29 14 1 2 0 46
10 77 57 1 11 0 146
Reference 0.75 (0.23–2.39) 0.49 (0.14–1.66) 2.00 (0.10–39.07) 0.36 (0.05–2.31)
0.631 0.254 0.648 0.284
0.63 (0.20–1.93)
0.420
(17.6) (49.0) (33.3) (66.7)
(9.8) (56.9) (27.5) (2.0) (3.9) (0) (90.2)
(6.4) (49.4) (36.5) (0.6) (7.1) (0) (93.6)
40 (39.2) 59 (57.8) 3 (2.9)
98 (31.4) 202 (64.7) 12 (3.8)
Reference 0.71 (0.44–1.14) 0.61 (0.16–2.28)
0.161 0.466
7 27 17 44
22 72 62 134
Reference 1.17 (0.45–3.07) 0.86 (0.31–2.35) 1.03 (0.41–2.58)
0.737 0.772 0.946
Reference 0.881 (0.55–1.39)
0.586
(13.7) (52.9) (33.3) (86.3)
41 (40.2) 61 (59.8)
P value not significant for both dominant and recessive models.
(14.1) (46.2) (39.7) (85.9)
116 (37.2) 196 (62.8)
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Table 6 Allelic and genotypic frequencies of MDR1 1236C>T, 2677G>T/A and 3435C>T among patients with dose delay/reduction due to neutropenia. Genotype MDR1 1236C>T CC CT TT Recessive model (TT vs. CT + CC)* Alleles C T MDR1 2677G>T/A GG GT TT GA AT AA Dominant model Alleles G T A MDR1 3435C>T CC CT TT Dominant model Allele C T *
Dose delay/reduction, n = 30 (14.5%)
No dose delay/reduction, n = 177 (85.5%)
OR (95%CI)
P
5 9 16 14
29 93 55 122
(16.4) (52.5) (31.1) (68.9)
Reference 0.56 (0.17–1.80) 1.68 (0.56–5.07) 2.53 (1.15–5.55)
0.333 0.351 0.02
19 (31.7) 41 (68.3)
151 (42.7) 203 (57.3)
Reference 1.60 (0.89–2.87)
0.112
3 14 12 0 1 0 27
12 92 59 2 12 0 165
(6.8) (52.0) (33.3) (1.1) (6.8) (0) (93.2)
Reference 0.60 (0.15–2.43) 0.81 (0.19–3.33) 0.00 0.33 (0.03–3.67) – 0.65 (0.17–2.47)
0.482 0.774 0.999 0.370 – 0.532
20 (33.3) 39 (65.0) 1 (1.7)
118 (33.3) 222 (62.7) 14 (4.0)
Reference 1.03 (0.57–1.85) 0.42 (0.05–3.38)
0.904 0.416
3 11 16 27
26 88 63 151
(14.7) (49.7) (35.6) (85.3)
Reference 1.08 (0.28–4.17) 2.20 (0.59–8.19) 1.55 (0.43–5.48)
0.907 0.240 0.497
140 (39.5) 214 (60.5)
Reference 1.65 (0.90–3.01)
0.100
(16.7) (30.0) (53.3) (46.7)
(10.0) (46.7) (40.0) (0.0) (3.3) (0) (90.0)
(10.0) (36.7) (53.3) (90.0)
17 (28.3) 43 (71.7)
Dominant model not shown as P value not significant. The significant values are shown in bold.
overall analysis did not show any association of this SNP with drug related toxicity [OR = 1.08(0.58–2.02), P = 0.81] (Fig. 2).
compared for both fixed and random models and there was not much difference in the results. Sensitivity analysis showed that the results of meta analysis were quite stable and consistent.
4.3. Sensitivity analysis 4.4. Bias diagnostics The sensitivity of meta analysis was checked by one study removed and see whether there was an effect in the overall OR or not. On removing one study the combined OR did not change much [OR = 0.84(0.59–1.18), P = 0.32]. The combined OR was also
For estimating publication bias, Begg’s funnel plot and egger’s test were conducted. The funnel plot shape for the pooled analysis of toxicity studies did not reveal any asymmetry (Supplementary
Fig. 1. Forest plots for the association of ABCB1 3435C>T, 1236C>T and 2677G>T/A polymorphism with response to chemotherapy in breast cancer patients.
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Fig. 2. Forest plots for the association of ABCB1 1236C>T, 3435C>T and 2677G>T/A polymorphism with drug induced toxicity in breast cancer patients.
Fig S2). The Egger’s regression intercept was 1.21, t value was 0.52, two sided P = 0.61. The statistical tests did not reveal any publication bias. See Figure S2 as supplementary file. Supplementary material related to this article found, in the online version, at http:// dx.doi.org/10.1016/j.canep.2013.04.012. 5. Discussion ln our study the ABCB1 1236C>T SNP was significantly associated with treatment outcomes in terms of response to chemotherapy and toxicity, while the other two SNPs 2677G>T/A and 3435C>T did not show any association with clinical response. Cancer treatment involves multi-modality protocol, out of which multidrug resistance is one of the critical phenomena that should be looked into. Polymorphisms in drug transporters which account for inherited variability, contribute significantly to multidrug resistance. In our study, we evaluated treatment response in one hundred patients and found that 1236C>T was significantly associated with response to chemotherapy. Till date there are only two reports published, suggesting no association of 1236C>T with response to chemotherapy in breast cancer patients of Chinese population [13,26]. We also studied the association of 2677G>T/A which is a triallelic SNP but could not find any association of this polymorphism with treatment response. Chang et al. [23] examined 2677G>T/A in 108 patients and found that the genotype was not correlated with response to chemotherapy. Ji et al. [26] also evaluated chemotherapy response in 153 patients but did not find any association of 26777G>T/A with response. Among the three SNPs, 3435C>T is the most studied ABCB1 polymorphism. In a report by Kafka et al., authors have demonstrated that 3435C>T is associated with pathological response to NACT in advanced breast cancer patients [11]. However, in our study we could not find any association of 3435C>T with response to NACT. George et al. [24] and Rodrigues et al. [2] did not find any association between 3435C>T polymorphisms and response to chemotherapy which is in agreement with our study. A study by Cizmarikova et al. [12] indicated that the C allele in 3435C>T significantly increased the response rate to chemotherapy. In view of contradicting reports,
we performed a meta analysis, which showed that there was no overall association of 3435C>T polymorphism with treatment response. On evaluating 207 patients who suffered from grade 2–4 toxicity on the basis of anemia, leucopenia and thrombocytopenia, we found that one of the three polymorphisms (1236C>T) studied showed significant association. For 1236C>T, there are three reports till date which correlate genotype with breast cancer chemo-toxicity. These studies demonstrated that 1236C>T genotype was not associated with drug induced toxicity [13,14]. Similarly, studies in 2677G>T/A polymorphism could not find any association with toxicity. However, 3435C>T polymorphism was associated with toxicity in terms of leucopenia [14]. In a study by Cizmarikova et al. [12], no association of 3435C>T polymorphism was found with hematological toxicities. However in our study, we find significant association of 1236C>T with chemo-toxicity. Dose delay/reduction due to febrile neutropenia is also a measure of toxicity, hence we also evaluated the association of ABCB1 polymorphisms with dose delay/reduction. Significant association with dose delay/reduction was seen in the recessive model for 1236C>T. To draw a definitive conclusion, we performed a meta analysis of the previous studies which revealed no overall association of ABCB1 1236C>T, 2677G>T/A and 3435C>T polymorphisms with grade 2–4 chemo-toxicity though there was a trend toward association for 1236C>T. Association between ABCB1 1236C>T polymorphism and treatment outcomes in terms of response to chemotherapy in our study may be due to the fact that the presence of variant allele in the ABCB1 gene may lead to a lower expression of P-gp, which further results in accumulation of drugs inside the cell, thus altering the distribution profile of the chemotherapeutic drugs inside cells. Consequently this may lead to toxicity, as we found association of 1236C>T polymorphism with drug related grade 2– 4 toxicity. The 1236C>T polymorphism was associated with dose delay and this dose delay may cause poor response in patients. In meta analysis also, there was trend toward association for 1236C>T with treatment response but the other two SNPs did not show any association, Therefore, ABCB1 polymorphisms do exert significant effects on breast cancer chemotherapy responses. However, it should be kept in mind that not a single gene and its genetic polymorphisms alone are sufficient to determine the
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response to chemotherapy and chemo-toxicity. Other factors that regulate the enzymatic modification of drugs or drug targets, and alternations in molecular pathways regulating cellular response to drug induced toxicity also participate in determining the effect of drugs [25]. In near future, more studies with a pharmacogenetic approach involving whole drug metabolism, transport and drug targets are expected to ascertain the role of genetic variants in predicting treatment outcomes. In conclusion, ABCB1 genetic variations especially 1236C>T showed association with poor response to anthracycline based chemotherapy, which may be due to dose reduction or delay resulting from the toxicity caused by higher accumulation of drug inside the cell in breast cancer patients. However, this observation needs to be validated in larger cohort before clinical applications. Conflicts of interest The authors declare that there are no conflicts of interest. Acknowledgements We are thankful to Indian Council of Medical Research and Department of Science and Technology for financial assistance to carry out this work. References [1] Evans WE, Relling MV. Pharmacogenomics: translating functional genomics into rational therapeutics. Science 1999;286:487–91. [2] Rodrigues FF, Santos RE, Melo MB, Silva MA, Oliveira AL, Rozenowicz RL, et al. Correlation of polymorphism C3435T of the MDR-1 gene and the response of primary chemotherapy in women with locally advanced breast cancer. Genet Mol Res 2008;7(1):177–83. [3] Evans WE, McLeod HL. Pharmacogenomics – drug disposition, drug targets, and side effects. N Engl J Med 2003;348:538–49. [4] Gottesman MM, Fojo T, Bates SE. Multidrug resistance in cancer: role of ATP dependent transporters. Nat Rev Cancer 2002;2:48–58. [5] Burger H, Foekens JA, Look MP, Gelder ME, Klijn JG, Wiemer EA, et al. RNA expression of breast cancer resistance protein, lung resistance-related protein, multidrug resistance-associated proteins 1 and 2, and multidrug resistance gene 1 in breast cancer: correlation with chemotherapeutic response. Clin Cancer Res 2003;9:827–36. [6] Mutoh K, Tsukahara S, Mitsuhashi J, Katayama K, Sugimoto Y. Estrogen mediated post transcriptional down-regulation of P-glycoprotein in MDR1transduced human breast cancer cells. Cancer Sci 2006;97:1198–204. [7] Chen CJ, Clark D, Ueda K, Pastan I, Gottesman MM, Roninson IB. Genomic organization of the human multidrug resistance (MDR1) gene and origin of Pglycoproteins. J Biol Chem 1990;265:506–14.
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